A video analytics algorithm, system, and method. A motorway, i.e., expressway or highway, ramp safety warning method is used that is free from environmental constraints, permitting all vehicles to be included in the range of real-time video, thus providing a reliable and early warning. The method is easy to implement, is highly accurate, is suitable for real-time traffic safety warning for any highway or motorway, and thus has broad application.
Prior art methods lack these features and benefits, instead disclosing traditional safety surrogate measures that lack efficient and/or effective capture of real-time traffic conflicts in the context of multiple moving vehicles, such as at intersections.
As one example, CN 103236191 discloses a video-based safety precaution method for vehicles merging from a highway ramp using a time difference conflict possibility. It incorporates a security alarm video-based vehicle freeway ramp through the exit ramp where two cameras detect motion of the vehicle and two roads in the same direction, calibrate vehicle trajectory based on continuous tracking frame vehicle trajectory through the operation to obtain the actual movement distance, and then obtain the actual speed of the vehicle. The incorporated area of the time difference by the speed of the two vehicles on the road determines the vehicle time difference conflict possibility.
As another example, WO 2014/020315 detects a moving vehicle by receiving image data representing a sequence of image frames over time. It analyzes the image data to identify potential moving vehicles, and compares the potential moving vehicle with a vehicle movement model that defines a trajectory of a potential moving vehicle to determine whether the potential moving vehicle conforms with the model.
As another example, US 2011/0071750 detects vehicles including aircraft by reducing a vehicle travel path in a three dimensional space to a first dimension; receiving data corresponding to a motion of the vehicle, i.e., aircraft; mapping the motion to the vehicle travel paths in the first dimension; and transmitting an alert if a potential conflict is determined in the vehicle travel paths in the first dimension.
Given the complexity and subtlety of conflict events (e.g., a dangerous near-miss scenario), a human observer has conventionally been required to detect a true conflict. Recent focus is on automating conflict identification and quantification using a safety surrogate measure such as time-to-collision (TTC), post-encroachment time (PET), potential time to collision (PTTC), difference in vehicle speeds (DeltaS), initial deceleration rate of the second vehicle (DR), the maximum deceleration of the second vehicle (MaxD), difference in velocities (DeltaV), and safe Stopping Distance (SSD). The Federal Highway Administration developed a surrogate safety assessment model [I], which allows for an expedited safety assessment. Micro-simulation models to extract vehicle trajectories, and a significant number of simulation runs, are conventionally required for meaningful statistical inferences. Other research studies have extracted surrogate measures from video images based on spatial or temporal proximity of two or more conflicting road users. An Extended Delta V measure has been proposed to integrate the proximity to a crash, as well as the outcome severity in the event a crash would have taken place, both of which are important dimensions in defining the severity of a traffic conflict event. Prior art methods typically use one or more simplified indicators (e.g., TTC, PET, DR, etc.) to identify a conflict event, but each indictor has drawbacks. Given the complexity, variety, and subtlety of conflict events, a true conflict may not be identifiable by any of those indictors because those indicators were mostly based on partial aspects of conflict events. Simulation-based conflict analysis relies on predictive modeling of trajectories, is computationally demanding, is not suited for real-time applications, and has questionable accuracy and reliability. Conflict severity has been estimated based on an Extended Delta V, which assumes that the two road users spent the time available to brake before arriving at the collision point. For this reason, driver behaviors (e.g., deceleration rates) and collision mechanism (e.g., inelastic collision) have to be assumed to calculate the metric.